Learning to Share: Engineering Adaptive Decision-Support for Online Social Networks

Paper i proceeding, 2017

Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.

Författare

Yasmin Rafiq

Imperial College London

Alessandra Russo

Imperial College London

Luke Dickens

University College London (UCL)

Arosha K. Bandara

Open University

Blaine A. Price

Open University

Bashar Nuseibeh

University of Limerick

Open University

Avelie Stuart

University of Exeter

Mark Levine

University of Exeter

Mu Yang

University of Southampton

Gul Calikli

Chalmers, Data- och informationsteknik, Software Engineering

Göteborgs universitet

PROCEEDINGS OF THE 2017 32ND IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE'17)